Search

The data rate of airborne radar is much higher than the wireless data transfer rate in many detection applications, so the onboard data storage systems are usually used to store the radar data. Data storage systems with good seismic performance usually use NAND Flash as storage medium, and there is a widespread problem of long file management time, which seriously affects the data storage speed, especially under the limitation of platform miniaturization. To solve this problem, a data storage method based on machine learning is proposed for mini-airborne radar. The storage training model is established based on machine learning, and could process various kinds of radar data. The file management methods are classified and determined using the model, and then are applied to the storage of radar data. To verify the performance of the proposed method, a test was carried out on the data storage system of a mini-airborne radar. The experimental results show that the method based on machine learning can form various data storage methods adapted to different data rates and application scenarios. The ratio of the file management time to the actual data writing time is extremely low.

Synthetic transmit aperture (STA) imaging can achieve optimal lateral resolution in the full field of view, at the cost of low frame rate (FR) and low signal-to-noise ratio (SNR). In our previous studies, compressed sensing based synthetic transmit aperture (CS-STA) and minimal l2-norm least squares (LS-STA) methods were proposed to recover the complete STA dataset from fewer Hadamard-encoded plane wave (PW) transmissions. Results demonstrated that, compared with STA imaging, CS/LS-STA can maintain the high resolution of STA and improve the contrast in the deep region with increased FR. However, these methods would introduce errors to the recovered STA datasets and subsequently produce severe artifacts to the beamformed images. Recently, we discovered that the theoretical explanation for the error introduced in the LS-STA-based recovery is that LS-STA method neglects the null space component of the real STA data. To deal with this problem, we propose to train a convolutional neural network (CNN) under the null space learning framework (to estimate the missing null space component) for high-accuracy recovery of the STA dataset from fewer Hadamard-encoded PW transmissions. The mapping between the low-quality STA dataset (recovered using the LS-STA method) and the corresponding high-quality STA dataset (obtained using full Hadamard-encoded STA imaging, HE-STA) was learned from phantom and in vivo samples. The performance of the proposed CNN-STA method was compared with the LS-STA, STA, and HE-STA methods, in terms of visual quality, NRMSE, gCNR, and FWHM. The results demonstrate that the proposed method can improve the recovery accuracy of the STA datasets and therefore effectively suppress the artifacts presented in the images obtained using the LS-STA method. In addition, the proposed method can maintain the high lateral resolution of STA with fewer PW transmissions, as LS-STA does.

Due to the modulation of tiny frequency offset on the array elements, a frequency diverse array (FDA) jammer can generate multiple range-dimension point false targets, and many deception jamming methods against SAR using an FDA jammer have been studied. However, the potential of the FDA jammer to generate barrage jamming has rarely been reported. In this paper, a barrage jamming method against SAR using an FDA jammer is proposed. To achieve two-dimensional (2-D) barrage effect, the stepped frequency offset of FDA is introduced to generate range-dimensional barrage patches, and the micro-motion modulation is employed to increase the extent of barrage patches along the azimuth direction. Mathematical derivations and simulation results demonstrate the validity of the proposed method in generating flexible and controllable barrage jamming.

Background: Realgar, a traditional Chinese medicine, has shown antitumor efficacy in several tumor types. We previously showed that realgar nanoparticles (nano-realgar) had significant antileukemia, anti-lung cancer and anti-liver cancer effects. In addition, the anti-tumor effects of nanorealgar were significantly better than those of ordinary realgar. Objective: To explore the inhibitory effects and molecular mechanisms of nano-realgar on the migration, invasion and metastasis of mouse breast cancer cells. Methods: Wound-healing migration assays and Transwell invasion assays were carried out to determine the effects of nano-realgar on breast cancer cell (4T1) migration and invasion. The expression levels of matrix metalloproteinase (MMP)-2 and -9 were measured by Western blot. A murine breast cancer metastasis model was established, administered nano-realgar for 32 days and monitored for tumor growth and metastasis by an in vivo optical imaging system. Finally, living imaging and hematoxylin and eosin (HE) staining were used to measure the morphology and pathology of lung and liver cancer cell metastases, respectively. Angiogenesis was assessed by CD34 immunohistochemistry. Results: Nano-realgar significantly inhibited the migration and invasion of breast cancer 4T1 cells and the expression of MMP-2 and -9. Meanwhile, nano-realgar effectively suppressed the abilities of tumor growth, metastasis and angiogenesis in the murine breast cancer metastasis model in a time- and dosedependent manner. Conclusion: Nano-realgar significantly inhibited migration and invasion of mouse breast cancer cells in vitro as well as pulmonary and hepatic metastasis in vivo, which may be closely correlated with the downexpression of MMP-2 and -9 and suppression of tumor neovascularization.